# Myocardial radiomics of non-ischemic cardiomyopathy using cardiovascular magnetic resonance: current perspectives and future directions

## TL;DR

This paper reviews how radiomics and machine learning can improve the diagnosis of non-ischemic cardiomyopathy using cardiovascular magnetic resonance imaging.

## Contribution

The paper introduces novel ML-based radiomic approaches for non-ischemic cardiomyopathy diagnosis without contrast agents.

## Key findings

- Radiomic analysis can extract features beyond visual perception for detailed myocardial evaluation.
- Machine learning workflows show potential for accurate diagnosis of non-ischemic cardiomyopathy.
- Integration of radiomics and ML may reduce reliance on contrast agents in CMR.

## Abstract

Heart failure remains a major source of global morbidity and mortality, frequently driven by the structural and functional myocardial changes associated with ischemic and non-ischemic cardiomyopathies. While cardiovascular magnetic resonance (CMR) is the gold standard for non-invasive ventricular assessment, standard clinical measures rely on visual human interpretation. By contrast, radiomic analysis, a high-throughput computational approach that can extract quantitative features beyond the limits of visual perception, has gained interest in its application to CMR for detailed evaluation of myocardial properties. Over the last decade, novel studies integrating radiomics with machine learning (ML) algorithms may enable more accurate diagnosis and personalized characterization of non-ischemic cardiomyopathy beyond traditional CMR sequences, and without the use of gadolinium-based contrast agents. This review provides an overview of CMR radiomic analysis, summarizes recent applications of ML workflows in non-ischemic cardiomyopathy, and discusses the challenges and opportunities in integrating these computational tools into clinical practice.